arXiv Computation and Language August 25, 2026 By Sike Xiang, Shuang Chen, Kevin Qinghong Lin, Jialin Yu, Yijia Sun, Philip Torr, Amir Atapour-Abarghouei

Checkup2Action: A Multimodal Clinical Check-up Report Dataset for Patient-Oriented Action Card Generation

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arXiv AI

Aug 5

CARE-Bench: Benchmarking Patient-Facing LLM Triage

arXiv:2608. 03731v1 Announce Type: new Abstract: Patient-facing medical LLMs and agents increasingly answer symptom questions before clinician contact, where the key safety question is what action the user should take next.

By Yining Hua, Hongbin Na, Cyrus Ayubcha

llms agents benchmarks safety

arXiv AI

Jul 13

MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation

arXiv:2607. 09142v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in online medical consultation, yet existing benchmarks remain poorly aligned with real clinical practice.

By Runhan Shi, Quan Zhou, Yuqian Xu, Shuai Yang, Xin Wu, Zitong Zhou, Hui Liu, Bin Cha, Zheming Wang, Liya Li, Wei Wei, Haoyuan Hu, Jun Xu

llms multimodal benchmarks safety

arXiv AI

6d ago

G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation

arXiv:2608. 20331v1 Announce Type: cross Abstract: Personalized interpretation of medical reports has emerged as an increasingly important need among patients.

By Shiao Xie, Siyu Chen, Jianwei Lv, Bo Yuan, Yujin Wang, Xiandong Li

reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 19

MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization

arXiv:2606. 20164v1 Announce Type: cross Abstract: Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical questions.

By Aueaphum Aueawatthanaphisut

llms rag agents nlp multimodal

arXiv AI

Jul 3

MedRepBench: A Comprehensive Benchmark for Medical Report Interpretation

arXiv:2508. 16674v2 Announce Type: replace-cross Abstract: Medical report understanding from real-world document images is essential for generating patient-facing explanations and enabling structured information exchange in clinical systems.

By Fangxin Shang, Yuan Xia, Dalu Yang, Yahui Wang, Binglin Yang

llms computer-vision multimodal benchmarks safety

arXiv AI

Jul 13

Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs

arXiv:2508. 14817v2 Announce Type: replace-cross Abstract: Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).

By Skatje Myers, Dmitriy Dligach, Timothy A. Miller, Samantha Barr, James Landefeld, Yanjun Gao, Matthew Churpek, Anoop Mayampurath, Majid Afshar

llms rag

One email a morning, machine-written

Related stories

CARE-Bench: Benchmarking Patient-Facing LLM Triage

MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation

G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation

MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization

MedRepBench: A Comprehensive Benchmark for Medical Report Interpretation

Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs